reduction for private vehicles. The micro simulation model was developed 10 conduct traffic impact analysis afler !he implementation of !he policy.
Scope of traffic impact are travelling speed in road section and walking limes in pedestrian crossing, name Safery School Zone (SSZ) Area. The model resulls show 1ha1 !he application of green campus concept does nol improve the 1ransporta1ion performance around !he campus ..
Keywords: UI Greenmetric, .micro simulation model, travelling speed, walking speed
1 INTRODUCTION 1.1 Study Background
Green
A developing city will have an increase in activity movement by its citizens, and often cause problems. World Commission for Environment and Development (1987) explains that transpo1tation is a problem that often oc.curs in developing cities, because this aspect is closely related to economic and other activities conducted by the community on a regular basis.
Choi (2017) explains that the university is a small scale of a developing city. Often the university becomes an experiment for testing the effectiveness of policies on sustainable issues, so that it can be applied on a larger scale later. In addition, students as one university community will become expe1ts, policy-makers or other professions after graduation, so it becomes impo1tant to familiarize life sustainably. Sisriany (2017) explains that the concept of green campus is a concept to make the campus community to live sustainability and friendly to the surrounding environment.
Examples of transportation problems within the university's scope are the number of vehicles driving down or raising d1e passengers, the need for parking spaces that are unable to service the demand, the university community using private vehicles or the number of road buffers in any place. VI Greenmetric (2016) is a tool to measure the level of
Transport categories are divided into sub-sections, which can be seen in Table I.
Table 1. Transportation Categories in UI Green Metric.
Code Categories and Indicator
TRI e ratio of vehicles (cars and motorcycles) towards campus population TR2 The ratio of campus bus services towards campus population
TR3 The ratio of bicycles round towards campus population TR4 Parking area type
TRS Initiatives to decrease private vehicles on campus
TR6 king area reduction for private vehicles within 3 years TR7 Campus bus services
TR8 Bicycle and pedestrian policy on campus
There are eight sub-sections, which aim
a
to reduce the use of rivate vehicles within the campus, so that the concept of sustainable transpo1tation can be applied. One of the sub categories to be studied is the impact of green campus policy in the form of parki.ng area reduction for private vehicles on transportation performance around ITENAS.1.2 Scope and Study Area
This study aims to identify the impacts that occur due to parking area reduction for private vehicles around the university, in this c.ase is the campus of National Institute of Technology Bandung (ITENAS). ITENAS is in Hajj Hasan Penghulu Mustopa (PHH Mustafa) Sti·eet. From the function of land use, it is including into the education area. There is an elementary school across from ITENAS and some high schools alongside ITENAS. In addition, there are eating places, stationery and student residence across from ITENAS.
This ce1tainly has an impact on the high intensity of road crossing, which has been facilitated by the government with the School Safety Zone (SSZ) Area.
Sc;hool Safety Zone Area
Fig 1 Map of ITENAS
According to the Department of Transpo1tation (2009), SSZ Area is an innovative program in the form of time-based velocity zones that can be used to regulate vehicle speed in the school area. The use of traffic engineering such as traffic signs and road markings and speed resb·ictions aimed at increasing the driver's attention to decreasing speed limits in the
SSZ illustration can be seen in Fig 2.
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Fig 2 School Safety Zone Area Illustration
Based on the location of the study, the scope of transpo1tation problems is Penglmlu Haji Hasan Mustafa (PHH Mustafa) Street and crossing in SSZ Area. Microscopic traffic modelling is used for indicators of both scopes. Cvitanic (2012) uses speed to as an indic.ator of road performance. Marisamynathan (2013) explains that pedestrian belrnviour can be described as walking speed (km/h). Both indicators will be used in this study as road and crossing performance.
2 DATA COLLECTION
PHH Mustafa Street is the primary arterial road with type 4/2 UD as delivered in Fig 3. The result of traffic volume calculation obtained by vehicle volume equal to 2,737 veh /hour and 2.819 veh/hour each for direction of movement toward Surapati and direction of movement toward PHH Mustafa with proportion of vehicle delivered on Fig 4.
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Fig 3 Road Geometric Condition
Fig 4 Vehicle Composition in Screen Line Survey
Based on sample standard deviations, that is explained in Quiroga (1998), with permitted error of 5 km I hour, sample standard deviations (s) is 3 and confidence level 95%, 60 sample each vehicle and pedestrian.
Spot speed survey at the location indicates that the average spot speed of motorcycles, light vehicles and vehicles is 36 km/h, 30 km/h and 23 km I h, respectively. Next Fig 5 displays the percentage of vehicles that move at a speed above a certain speed value. For example, 44.9% of motorcycles travel at speeds of more than 37 km/h, while for light vehicles and heavy vehicles, 34.5% move at speeds of more than 32 and 24 km I h.
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32 SPOT SPEED I KMJ\iOURI 37 42 47 52 51 62Fig 5 Percentage Cummulative Spot Speed for each Vehicle
The pedestrian counting survey of the crossers show that during the rush hour there are 295 people crossing from Itenas campus to SDN Sukasenang whereas as many as 458 people crossing from the opposite direction. In addition, the average speed of the pedestriitn is I. I 4 ,8 k.m/ho ur.
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Fig 6 Box Plot in Pedestrian Speed Survey
3 MODEL DEVELOPMENT
ODOT (2011) explained that the development of microsirnulatio1i model with VIS SIM software can be network and conb·oJ coding, dynamic vehicle routing, speed control coding and defining driving behavior model. Network coding is the step of making the link in accon:lance with the scope of the study area, giving the signal time (if there is a signal intersection) and setting the priority rule on the intersection of the intersection. Geomeb·ic data obtained from the survey is used in the network coding stage. Dynamic vehicle routing is the stages of setting the origin and destination routes in accordance with the data of origin of destination. There are five zoning of origin of destination made in this study, as can be seen in Figure 7.
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Zone Names
1. PHH Mustafa Street 2. Pahlawan (S) Street 3. Surapati Street 4. Pahlawan (U) Street 5. ITENAS University ll ... ,., -..,
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obtai.ned at spot speed survey results. Zhang (2016) also entered the desired speed data input using d1e cumulative frequency spot speed. ODOT (2011) also explains about driving behaviour model consisting of car following and lane changing. Qiao (2012) makes it clear that the two pa1ts of behaviour model have 192 parameters that can be modified in the VISSIM software. Qiao (2012) recommends the 14 most impo1tant parameters, and is a representation of car-following model, lane changing model and lane properties. lrawan (2015) also recommends 7 parameters performed calibration. Based on the recommendations of Qiao (2012) and lrawan (2015), the following are the parameters that
can be seen in Table 2.
Table 2. Parameters that Important to Consider in Analysis
No Parameters Data Range
I L erage Standstill Distance (m) 0,8
2 l. ditive Part of Desired Safety Distance 0,09
3 Multiplicative Part of Desired Safety Distance 0,02
4 Desired position at free flow Any
5 Overtake on same lane: on lefl & on right on
6 Distance standing (at 0 km/h) (m) 0,01
7 Distance driving (at 50 km/h) (m) 0,01
ODOT (2011) explains the next stage is calibration that is the process used to attain adequate parameter or validity of the model by establishing appropriate parameter values so the model replicates the local traffic conditions as closely as possible. There two step in calibration process. First, comparing observed and modelled traffic volume. The best universal to compare is GEH formula. For hourly, GEH formula is:
Wid1.
•M : simulated flow
•O : observed flow
(M - 0)2 GEH = , 0.5 X (M
+
0)There are six spot that considered in to be compared. Average GEH value is 1,9, which means model acceptable fit (GEH < 5,0) and at list 85% links within the calibration area and also with five times iteration. Further output can be seen at Table 3. Illustration of traffic condition can be seen at Figure 8.
Table 3 GEH Value No Movement
Spot Value
Type Observed Modelled GEH
I To PHH Mustafa Street 2819 2508 6.0
- 2 Vehicle To Surapati Street 2737 2783 0.9
- 3 Movement Exit Access Road ITENAS 35 39 0.7
- 4 Entrv Access Road ITENAS 46 34 1.9
5 Pedestrian Crossing from exit ITENAS 295 264 1.9
- 6 Crossing Crossing from entrv ITENAS 458 450 0.4
Avera2e 1.9
A
Fig 8 Illustration of Existing Traffic Condition
4 ANALYSIS OF GREEN CAMPUS CONCEP~MPLEMENTATION
The developed scenario comes from the reduction parking area policy for private vehicles within 3 years. VI Greenmetric (2016). In the explanation, there are three stages to reduce private vehicle, which is 10% reduction of parking area, followed by 30% parking area and there should be no parking inside the campus, hereinafter named as shown in Table 4.
Table 4. Scenario Development
No s.cenario Name
I Do Nothing (ON): Existing Condition
2 Do Something (OS I): Less than I 0% decrease 3 Do Something (OS 2): Between 10% -30% decrease 4 Do Something (OS 3): Parking is not pem1itted in campus
Performance to be measured are average speed, signalised intersection and walking speed, with the location can be seen at Figlffe 9 and JO. Traveling speed is measured along 227 m, for an average of all vehicles and both directions, which pass through two road side friction namely the ITENAS Entry-Entry Access Road and SSZ Area. Walking speed is measured along the 14 m, when it begins to cross to completion. Table 5 and Table 6 show the performance results for each development scenario.
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SMK MtultkOFig 9 Signalised Jn1ersec1ion near ITENAS
Table 5. Travelling Speed for Each Scenario
Travelling Speed (km/hour) Walking Speed (km/hour)
DN DSI DS2 DS3 DN DSI DS2 DS3 7.54 7.54 7.70 7.55 0.54 0.54 0.54 0.54 7.52 7.52 7.59 7.37 0.52 0.52 0.52 0.52 7.38 7.68 7.35 7.42 0.54 0.54 0.54 0.54 7.31 7.37 7.59 7.41 0.53 0.53 0.53 0.53 7.19 7.29 7.18 7.19 0.53 0.53 0.53 0.53 7.38 7.48 7.47 7.38 0.53 0.53 0.53 0.53
Table 6. Signalised lnlerseclion Perfo1TI1ance for Each Scenario
No. Iteration
Average Queue Length (m) Average Delay (s)
DN DSI DS2 DS3 DN DSI DS2
I 164.52 162.35 164.30 161.64 100.35 94.53 118.85 2 164.31 164.34 163.15 163.53 100.97 99.99 115.79 3 164.97 163.12 164.77 161.54 102.57 100.46 118.95 4 165.41 163.75 163.47 162.10 102.61 99.46 116.64 5 164.46 164.73 166.08 163.23 104.25 101.69 125.74 Average 164.74 163.66 164.36 162.41 102.15 99.23 119.19
•
DS 3
98.51 102.37
99.94 101.90 103.86 101.32
performance, both for traveling speed and walking speed. The average traveling speed ranges from 7.19-7.54 km I h. For signalised intersection performance, DN condition have higher queue length and delay towm·d DS condition, with percentage difference 1.2%. This is an indication that the category under which green campus is based on UI Green metric (2016) is not related to the improvement of transportation performance around the campus. It is necessary to develop categories in other parameters for the assessment of green campus aspects, so that it can have direct benefits, especially on the aspects of transportation ..
5 CONCLUSION
Transportation problems that occur in developing cities often occur as the impact of increased community activity. The university, as a small part of the city, is often a test site for testing the effectiveness of policies on sustainable issues, so that it can be applied on a large.r scale later.
The policy review to address the ongoing problems on campus is set out in the Green Campus concept, for example UI Greenmeti·ic (2016). One of the sub categories in the Greenmeti·ic UI is parking area reduction for private vehicles, which will further examine their impact on tl«inSport performance around ITENAS. Transport performance is limited to road performance in front of ITENAS, PHH Mustafa Sti·eet and pedestJ"ian performance in SSZ Area, with indicator of tl«iVeling speed and walking speed.
Microscopic tl«lffic modelling with VISSIM is done to find out the two performance. The developed scenarios come from parking policy. The results show that there is no difference in performance, both for tl«iVeling speed at PHH Mustafa Sti·eet and walking speed SSZ Area.